Abstract: Embodiments described herein provide approaches for detecting anomalies in electronic communications. Specifically, a set of response metrics is obtained from each of a plurality of communications. Each of the plurality of communications is part of a communications batch. Based on an aggregation of the response metric over the plurality of communications, a response metric value for one or more of the set of response metrics is computed. This response metric value is compared to a predicted metric value corresponding to the response metric. Based on this comparison, it is determined whether the response metric value is anomalous. If the response metric value is anomalous, a remediation activity is performed.

Abstract: Mechanisms are provided to implement an engagement optimization engine for generating and implementing a personalized patient engagement plan to monitor a patient's adherence to a personalized patient care plan. The engagement optimization engine receives a cohort of patients, each having different personal characteristics. For each patient, the engagement optimization engine identifies a set of patient-specific factors and a set of program-specific factors associated with a set of programs available to the patient. The engagement optimization engine evaluates each set of patient-specific factors and the program-specific factors to generate the personalized patient engagement plan for each patient to meet the needs of the patient. The engagement optimization engine outputs the personalized patient engagement plan for each patient to an assessor in order to implement monitoring of the personalized patient care plan generated for the patient.

Abstract: Embodiments described herein provide approaches for detecting anomalies in electronic communications. Specifically, a set of response metrics is obtained from each of a plurality of communications. Each of the plurality of communications is part of a communications batch. Based on an aggregation of the response metric over the plurality of communications, a response metric value for one or more of the set of response metrics is computed. This response metric value is compared to a predicted metric value corresponding to the response metric. Based on this comparison, it is determined whether the response metric value is anomalous. If the response metric value is anomalous, a remediation activity is performed.